Food delivery riders are urging platforms to disclose the inner workings of the algorithmic systems that determine their compensation. Workers for Deliveroo, Uber Eats, and Just Eat claim their earnings have declined as these companies have expanded their use of automation. One Edinburgh-based cyclist, who has spent seven years in the gig economy, reports earning approximately half of what he made four years ago despite working identical hours. He joined fellow riders in Bristo Square to discuss their deteriorating pay packets. Many have observed a gradual erosion of their income despite maintaining comparable delivery volumes, attributing these changes to increased reliance on artificial intelligence.
The Workers' Observatory, a charitable organisation established by gig workers in conjunction with academics from St Andrews and Edinburgh universities, is providing support. Its mission involves investigating aspects of platform work that remain opaque to those delivering the services. Xabier Villares, the observatory's principal organiser and an eight-year veteran of food delivery, describes a transformation over the past three years. Formerly, working four or five evenings weekly enabled him to meet rental and utility obligations comfortably. This is no longer feasible. The organisation has recently obtained decade-long research funding to continue its investigations.
The riders are seeking transparency regarding dynamic pricing mechanisms, which calibrate worker remuneration according to real-time market conditions. Research conducted by the University of Oxford and Columbia Business School demonstrated that Uber drivers experienced substantially reduced hourly earnings following the introduction of a new algorithmic pricing model in 2023. Drivers from Britain, the Netherlands, and elsewhere have initiated class action proceedings against the company in Amsterdam, where damages could reach billions of dollars. Cailean Gallagher, director of the observatory and a business school lecturer at St Andrews, observes that platforms obscure significant operational infrastructure and data from workers. His objective is to provide riders with foundational understanding of how these mechanisms function.